Exploring the MTPL2 Dataset
This workbook was created using the “dataexpks” template:
https://github.com/DublinLearningGroup/dataexpks
1 Introduction
This workbook performs the basic data exploration of the dataset.
2 Load Data
First we load the dataset.
rawdata_tbl <- read_rds("data/modelling2_data_tbl.rds") %>% select(-sev_data)
rawdata_tbl %>% glimpse()## Rows: 678,019
## Columns: 13
## $ pol_id <chr> "1", "3", "5", "10", "11", "13", "15", "17", "18", "21", …
## $ exposure <dbl> 0.10, 0.77, 0.75, 0.09, 0.84, 0.52, 0.45, 0.27, 0.71, 0.1…
## $ veh_power <chr> "5", "5", "6", "7", "7", "6", "6", "7", "7", "7", "7", "7…
## $ veh_age <int> 0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 1, 0, 9, 0, 2, 2, 2, …
## $ driv_age <int> 55, 55, 52, 46, 46, 38, 38, 33, 33, 41, 41, 56, 27, 27, 2…
## $ bonus_malus <int> 50, 50, 50, 50, 50, 50, 50, 68, 68, 50, 50, 50, 90, 90, 1…
## $ veh_brand <fct> B12, B12, B12, B12, B12, B12, B12, B12, B12, B12, B12, B1…
## $ veh_gas <chr> "Regular", "Regular", "Diesel", "Diesel", "Diesel", "Regu…
## $ area <fct> D, D, B, B, B, E, E, C, C, B, B, C, D, D, E, F, A, A, A, …
## $ density <int> 1217, 1217, 54, 76, 76, 3003, 3003, 137, 137, 60, 60, 173…
## $ region <fct> Rhone-Alpes, Rhone-Alpes, Picardie, Aquitaine, Aquitaine,…
## $ claim_total <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ claim_count <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
2.1 Perform Quick Data Cleaning
cleaned_names <- rawdata_tbl %>% names()
data_tbl <- rawdata_tbl %>% set_colnames(cleaned_names)
data_tbl %>% glimpse()## Rows: 678,019
## Columns: 13
## $ pol_id <chr> "1", "3", "5", "10", "11", "13", "15", "17", "18", "21", …
## $ exposure <dbl> 0.10, 0.77, 0.75, 0.09, 0.84, 0.52, 0.45, 0.27, 0.71, 0.1…
## $ veh_power <chr> "5", "5", "6", "7", "7", "6", "6", "7", "7", "7", "7", "7…
## $ veh_age <int> 0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 1, 0, 9, 0, 2, 2, 2, …
## $ driv_age <int> 55, 55, 52, 46, 46, 38, 38, 33, 33, 41, 41, 56, 27, 27, 2…
## $ bonus_malus <int> 50, 50, 50, 50, 50, 50, 50, 68, 68, 50, 50, 50, 90, 90, 1…
## $ veh_brand <fct> B12, B12, B12, B12, B12, B12, B12, B12, B12, B12, B12, B1…
## $ veh_gas <chr> "Regular", "Regular", "Diesel", "Diesel", "Diesel", "Regu…
## $ area <fct> D, D, B, B, B, E, E, C, C, B, B, C, D, D, E, F, A, A, A, …
## $ density <int> 1217, 1217, 54, 76, 76, 3003, 3003, 137, 137, 60, 60, 173…
## $ region <fct> Rhone-Alpes, Rhone-Alpes, Picardie, Aquitaine, Aquitaine,…
## $ claim_total <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ claim_count <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
2.2 Create Derived Variables
We now create derived features useful for modelling. These values are new variables calculated from existing variables in the data.
## Rows: 678,019
## Columns: 13
## $ pol_id <chr> "1", "3", "5", "10", "11", "13", "15", "17", "18", "21", …
## $ exposure <dbl> 0.10, 0.77, 0.75, 0.09, 0.84, 0.52, 0.45, 0.27, 0.71, 0.1…
## $ veh_power <chr> "5", "5", "6", "7", "7", "6", "6", "7", "7", "7", "7", "7…
## $ veh_age <int> 0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 1, 0, 9, 0, 2, 2, 2, …
## $ driv_age <int> 55, 55, 52, 46, 46, 38, 38, 33, 33, 41, 41, 56, 27, 27, 2…
## $ bonus_malus <int> 50, 50, 50, 50, 50, 50, 50, 68, 68, 50, 50, 50, 90, 90, 1…
## $ veh_brand <fct> B12, B12, B12, B12, B12, B12, B12, B12, B12, B12, B12, B1…
## $ veh_gas <chr> "Regular", "Regular", "Diesel", "Diesel", "Diesel", "Regu…
## $ area <fct> D, D, B, B, B, E, E, C, C, B, B, C, D, D, E, F, A, A, A, …
## $ density <int> 1217, 1217, 54, 76, 76, 3003, 3003, 137, 137, 60, 60, 173…
## $ region <fct> Rhone-Alpes, Rhone-Alpes, Picardie, Aquitaine, Aquitaine,…
## $ claim_total <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ claim_count <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
2.3 Check Missing Values
Before we do anything with the data, we first check for missing values in the dataset. In some cases, missing data is coded by a special character rather than as a blank, so we first correct for this.
With missing data properly encoded, we now visualise the missing data in a number of different ways.
2.3.1 Univariate Missing Data
We first examine a simple univariate count of all the missing data:
row_count <- data_tbl %>% nrow()
missing_univariate_tbl <- data_tbl %>%
summarise_all(list(~sum(are_na(.)))) %>%
gather("variable", "missing_count") %>%
mutate(missing_prop = missing_count / row_count)
ggplot(missing_univariate_tbl) +
geom_bar(aes(x = fct_reorder(variable, -missing_prop),
weight = missing_prop)) +
xlab("Variable") +
ylab("Missing Value Proportion") +
theme(axis.text.x = element_text(angle = 90))We remove all variables where all of the entries are missing
remove_vars <- missing_univariate_tbl %>%
filter(missing_count == row_count) %>%
pull(variable)
lessmiss_data_tbl <- data_tbl %>%
select(-one_of(remove_vars))With these columns removed, we repeat the exercise.
missing_univariate_tbl <- lessmiss_data_tbl %>%
summarise_all(list(~sum(are_na(.)))) %>%
gather("variable", "missing_count") %>%
mutate(missing_prop = missing_count / row_count)
ggplot(missing_univariate_tbl) +
geom_bar(aes(x = fct_reorder(variable, -missing_prop),
weight = missing_prop)) +
xlab("Variable") +
ylab("Missing Value Proportion") +
theme(axis.text.x = element_text(angle = 90))To reduce the scale of this plot, we look at the top twenty missing data counts.
missing_univariate_top_tbl <- missing_univariate_tbl %>%
arrange(desc(missing_count)) %>%
top_n(n = 50, wt = missing_count)
ggplot(missing_univariate_top_tbl) +
geom_bar(aes(x = fct_reorder(variable, -missing_prop),
weight = missing_prop)) +
xlab("Variable") +
ylab("Missing Value Proportion") +
theme(axis.text.x = element_text(angle = 90))2.3.2 Multivariate Missing Data
It is useful to get an idea of what combinations of variables tend to have variables with missing values simultaneously, so to construct a visualisation for this we create a count of all the times given combinations of variables have missing values, producing a heat map for these combination counts.
row_count <- rawdata_tbl %>% nrow()
count_nas <- ~ .x %>% are_na() %>% vec_cast(integer())
missing_plot_tbl <- rawdata_tbl %>%
mutate_all(count_nas) %>%
mutate(label = pmap_chr(., str_c)) %>%
group_by(label) %>%
summarise_all(list(sum)) %>%
arrange(desc(label)) %>%
select(-label) %>%
mutate(label_count = pmap_int(., pmax)) %>%
gather("col", "count", -label_count) %>%
mutate(miss_prop = count / row_count,
group_label = sprintf("%6.4f", round(label_count / row_count, 4))
)
ggplot(missing_plot_tbl) +
geom_tile(aes(x = col, y = group_label, fill = miss_prop), height = 0.8) +
scale_fill_continuous() +
scale_x_discrete(position = "top") +
xlab("Variable") +
ylab("Missing Value Proportion") +
theme(axis.text.x = element_text(angle = 90))This visualisation takes a little explaining.
Each row represents a combination of variables with simultaneous missing values. For each row in the graphic, the coloured entries show which particular variables are missing in that combination. The proportion of rows with that combination is displayed in both the label for the row and the colouring for the cells in the row.
2.4 Inspect High-level-count Categorical Variables
With the raw data loaded up we now remove obvious unique or near-unique variables that are not amenable to basic exploration and plotting.
coltype_lst <- create_coltype_list(data_tbl)
count_levels <- ~ .x %>% unique() %>% length()
catvar_valuecount_tbl <- data_tbl %>%
summarise_at(coltype_lst$split$discrete, count_levels) %>%
gather("var_name", "level_count") %>%
arrange(-level_count)
print(catvar_valuecount_tbl)## # A tibble: 6 x 2
## var_name level_count
## <chr> <int>
## 1 pol_id 678019
## 2 region 22
## 3 veh_power 13
## 4 veh_brand 12
## 5 area 7
## 6 veh_gas 3
## Dataset has 678019 rows
Now that we a table of the counts of all the categorical variables we can automatically exclude unique variables from the exploration, as the level count will match the row count.
unique_vars <- catvar_valuecount_tbl %>%
filter(level_count == row_count) %>%
pull(var_name)
print(unique_vars)## [1] "pol_id"
Having removed the unique identifier variables from the dataset, we may also wish to exclude categoricals with high level counts also, so we create a vector of those variable names.
highcount_vars <- catvar_valuecount_tbl %>%
filter(level_count >= level_exclusion_threshold,
level_count < row_count) %>%
pull(var_name)
cat(str_c(highcount_vars, collapse = ", "))We now can continue doing some basic exploration of the data. We may also choose to remove some extra columns from the dataset.
3 Univariate Data Exploration
Now that we have loaded the data we can prepare it for some basic data exploration. We first exclude the variables that are unique identifiers or similar, and tehen split the remaining variables out into various categories to help with the systematic data exploration.
## $split
## $split$continuous
## [1] "exposure" "veh_age" "driv_age" "bonus_malus" "density"
## [6] "claim_total" "claim_count"
##
## $split$discrete
## [1] "veh_power" "veh_brand" "veh_gas" "area" "region"
##
##
## $columns
## exposure veh_power veh_age driv_age bonus_malus veh_brand
## "continuous" "discrete" "continuous" "continuous" "continuous" "discrete"
## veh_gas area density region claim_total claim_count
## "discrete" "discrete" "continuous" "discrete" "continuous" "continuous"
3.1 Logical Variables
Logical variables only take two values: TRUE or FALSE. It is useful to see missing data as well though, so we also plot the count of those.
logical_vars <- coltype_lst$split$logical %>% sort()
for (plot_varname in logical_vars) {
cat("--\n")
cat(str_c(plot_varname, "\n"))
na_count <- explore_data_tbl %>% pull(!! plot_varname) %>% are_na() %>% sum()
explore_plot <- ggplot(explore_data_tbl) +
geom_bar(aes(x = !! sym(plot_varname))) +
xlab(plot_varname) +
ylab("Count") +
scale_y_continuous(labels = label_comma()) +
ggtitle(str_c("Barplot of Counts for Variable: ", plot_varname,
" (", na_count, " missing values)")) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
plot(explore_plot)
}3.2 Numeric Variables
Numeric variables are usually continuous in nature, though we also have integer data.
numeric_vars <- coltype_lst$split$continuous %>% sort()
for (plot_varname in numeric_vars) {
cat("--\n")
cat(str_c(plot_varname, "\n"))
plot_var <- explore_data_tbl %>% pull(!! plot_varname)
na_count <- plot_var %>% are_na() %>% sum()
plot_var %>% summary %>% print
explore_plot <- ggplot(explore_data_tbl) +
geom_histogram(aes(x = !! sym(plot_varname)),
bins = hist_bins_count) +
geom_vline(xintercept = mean(plot_var, na.rm = TRUE),
colour = "red", size = 1.5) +
geom_vline(xintercept = median(plot_var, na.rm = TRUE),
colour = "green", size = 1.5) +
xlab(plot_varname) +
ylab("Count") +
scale_y_continuous(labels = label_comma()) +
ggtitle(str_c("Histogram Plot for Variable: ", plot_varname,
" (", na_count, " missing values)"),
subtitle = "(red line is mean, green line is median)")
explore_std_plot <- explore_plot + scale_x_continuous(labels = label_comma())
explore_log_plot <- explore_plot + scale_x_log10 (labels = label_comma())
plot_grid(explore_std_plot,
explore_log_plot, nrow = 2) %>% print()
}## --
## bonus_malus
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 50.00 50.00 50.00 59.76 64.00 230.00 6
## --
## claim_count
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.03929 0.00000 66.00000
## --
## claim_total
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1 750 1172 2433 1346 4075401 653069
## --
## density
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1 92 393 1792 1658 27000 6
## --
## driv_age
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 18.0 34.0 44.0 45.5 55.0 100.0 6
## --
## exposure
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.002732 0.180000 0.490000 0.528750 0.990000 2.010000 6
## --
## veh_age
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 6.000 7.044 11.000 100.000 6
3.3 Categorical Variables
Categorical variables only have values from a limited, and usually fixed, number of possible values
categorical_vars <- coltype_lst$split$discrete %>% sort()
for (plot_varname in categorical_vars) {
cat("--\n")
cat(str_c(plot_varname, "\n"))
na_count <- explore_data_tbl %>% pull(!! plot_varname) %>% are_na() %>% sum()
plot_tbl <- explore_data_tbl %>%
pull(!! plot_varname) %>%
fct_lump(n = cat_level_count) %>%
fct_count() %>%
mutate(f = fct_relabel(f, str_trunc, width = 15))
explore_plot <- ggplot(plot_tbl) +
geom_bar(aes(x = fct_reorder(f, -n), weight = n)) +
xlab(plot_varname) +
ylab("Count") +
scale_y_continuous(labels = label_comma()) +
ggtitle(str_c("Barplot of Counts for Variable: ", plot_varname,
" (", na_count, " missing values)")) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
plot(explore_plot)
}## --
## area
## --
## region
## --
## veh_brand
## --
## veh_gas
## --
## veh_power
3.4 Date/Time Variables
Date/Time variables represent calendar or time-based data should as time of the day, a date, or a timestamp.
datetime_vars <- coltype_lst$split$datetime %>% sort()
for (plot_varname in datetime_vars) {
cat("--\n")
cat(str_c(plot_varname, "\n"))
plot_var <- explore_data_tbl %>% pull(!! plot_varname)
na_count <- plot_var %>% are_na() %>% sum()
plot_var %>% summary() %>% print()
explore_plot <- ggplot(explore_data_tbl) +
geom_histogram(aes(x = !! sym(plot_varname)),
bins = hist_bins_count) +
xlab(plot_varname) +
ylab("Count") +
scale_y_continuous(labels = label_comma()) +
ggtitle(str_c("Barplot of Dates/Times in Variable: ", plot_varname,
" (", na_count, " missing values)"))
plot(explore_plot)
}4 Bivariate Data Exploration
We now move on to looking at bivariate plots of the data set.
4.1 Facet Plots on Variables
A natural way to explore relationships in data is to create univariate visualisations facetted by a categorical value.
4.1.1 Logical Variables
For logical variables we facet on barplots of the levels, comparing TRUE, FALSE and missing data.
logical_vars <- logical_vars[!logical_vars %in% facet_varname] %>% sort()
for (plot_varname in logical_vars) {
cat("--\n")
cat(str_c(plot_varname, "\n"))
plot_tbl <- data_tbl %>% filter(!are_na(!! plot_varname))
explore_plot <- ggplot(plot_tbl) +
geom_bar(aes(x = !! sym(plot_varname))) +
facet_wrap(facet_varname, scales = "free") +
xlab(plot_varname) +
ylab("Count") +
scale_y_continuous(labels = label_comma()) +
ggtitle(str_c(facet_varname, "-Faceted Barplots for Variable: ",
plot_varname)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
plot(explore_plot)
}4.1.2 Numeric Variables
For numeric variables, we facet on histograms of the data.
for (plot_varname in numeric_vars) {
cat("--\n")
cat(str_c(plot_varname, "\n"))
plot_tbl <- data_tbl %>% filter(!are_na(!! plot_varname))
explore_plot <- ggplot(plot_tbl) +
geom_histogram(aes(x = !! sym(plot_varname)),
bins = hist_bins_count) +
facet_wrap(facet_varname, scales = "free") +
xlab(plot_varname) +
ylab("Count") +
scale_y_continuous(labels = label_comma()) +
ggtitle(str_c(facet_varname, "-Faceted Histogram for Variable: ",
plot_varname)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
print(explore_plot + scale_x_continuous(labels = label_comma()))
print(explore_plot + scale_x_log10 (labels = label_comma()))
}## --
## bonus_malus
## --
## claim_count
## --
## claim_total
## --
## density
## --
## driv_age
## --
## exposure
## --
## veh_age
4.1.3 Categorical Variables
We treat categorical variables like logical variables, faceting the barplots of the different levels of the data.
categorical_vars <- categorical_vars[!categorical_vars %in% facet_varname] %>% sort()
for (plot_varname in categorical_vars) {
cat("--\n")
cat(str_c(plot_varname, "\n"))
plot_tbl <- data_tbl %>%
filter(!are_na(!! plot_varname)) %>%
mutate(
varname_trunc = fct_relabel(!! sym(plot_varname), str_trunc, width = 10)
)
explore_plot <- ggplot(plot_tbl) +
geom_bar(aes(x = varname_trunc)) +
facet_wrap(facet_varname, scales = "free") +
xlab(plot_varname) +
ylab("Count") +
scale_y_continuous(labels = label_comma()) +
ggtitle(str_c(facet_varname, "-Faceted Histogram for Variable: ",
plot_varname)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
plot(explore_plot)
}## --
## area
## --
## veh_brand
## --
## veh_gas
## --
## veh_power
4.1.4 Date/Time Variables
Like the univariate plots, we facet on histograms of the years in the dates.
for (plot_varname in datetime_vars) {
cat("--\n")
cat(str_c(plot_varname, "\n"))
plot_tbl <- data_tbl %>% filter(!are_na(!! plot_varname))
explore_plot <- ggplot(plot_tbl) +
geom_histogram(aes(x = !! sym(plot_varname)),
bins = hist_bins_count) +
facet_wrap(facet_varname, scales = "free") +
xlab(plot_varname) +
ylab("Count") +
scale_y_continuous(labels = label_comma()) +
ggtitle(str_c(facet_varname, "-Faceted Histogram for Variable: ",
plot_varname))
plot(explore_plot)
}5 Custom Explorations
In this section you can add your own multivariate visualations such as boxplots and so on.
6 R Environment
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.2 (2020-06-22)
## os Ubuntu 20.04.1 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2021-02-04
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib source
## assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.0)
## backports 1.1.10 2020-09-15 [1] RSPM (R 4.0.2)
## blob 1.2.1 2020-01-20 [1] RSPM (R 4.0.0)
## bookdown 0.20 2020-06-23 [1] RSPM (R 4.0.2)
## broom 0.7.1 2020-10-02 [1] RSPM (R 4.0.2)
## cellranger 1.1.0 2016-07-27 [1] RSPM (R 4.0.0)
## cli 2.1.0 2020-10-12 [1] RSPM (R 4.0.2)
## colorspace 1.4-1 2019-03-18 [1] RSPM (R 4.0.0)
## conflicted * 1.0.4 2019-06-21 [1] RSPM (R 4.0.0)
## cowplot * 1.1.0 2020-09-08 [1] RSPM (R 4.0.2)
## crayon 1.3.4 2017-09-16 [1] RSPM (R 4.0.0)
## DBI 1.1.0 2019-12-15 [1] RSPM (R 4.0.0)
## dbplyr 1.4.4 2020-05-27 [1] RSPM (R 4.0.0)
## digest 0.6.25 2020-02-23 [1] RSPM (R 4.0.0)
## dplyr * 1.0.2 2020-08-18 [1] RSPM (R 4.0.2)
## ellipsis 0.3.1 2020-05-15 [1] RSPM (R 4.0.0)
## evaluate 0.14 2019-05-28 [1] RSPM (R 4.0.0)
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## farver 2.0.3 2020-01-16 [1] RSPM (R 4.0.0)
## forcats * 0.5.0 2020-03-01 [1] RSPM (R 4.0.0)
## fs * 1.5.0 2020-07-31 [1] RSPM (R 4.0.2)
## generics 0.0.2 2018-11-29 [1] RSPM (R 4.0.0)
## ggplot2 * 3.3.2 2020-06-19 [1] RSPM (R 4.0.1)
## glue 1.4.2 2020-08-27 [1] RSPM (R 4.0.2)
## gtable 0.3.0 2019-03-25 [1] RSPM (R 4.0.0)
## haven 2.3.1 2020-06-01 [1] RSPM (R 4.0.2)
## hms 0.5.3 2020-01-08 [1] RSPM (R 4.0.0)
## htmltools 0.5.0 2020-06-16 [1] RSPM (R 4.0.1)
## httr 1.4.2 2020-07-20 [1] RSPM (R 4.0.2)
## jsonlite 1.7.1 2020-09-07 [1] RSPM (R 4.0.2)
## knitr 1.30 2020-09-22 [1] RSPM (R 4.0.2)
## labeling 0.3 2014-08-23 [1] RSPM (R 4.0.0)
## lifecycle 0.2.0 2020-03-06 [1] RSPM (R 4.0.0)
## lubridate * 1.7.9 2020-06-08 [1] RSPM (R 4.0.2)
## magrittr * 1.5 2014-11-22 [1] RSPM (R 4.0.0)
## memoise 1.1.0 2017-04-21 [1] RSPM (R 4.0.0)
## modelr 0.1.8 2020-05-19 [1] RSPM (R 4.0.0)
## munsell 0.5.0 2018-06-12 [1] RSPM (R 4.0.0)
## pillar 1.4.6 2020-07-10 [1] RSPM (R 4.0.2)
## pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.0.0)
## ps 1.4.0 2020-10-07 [1] RSPM (R 4.0.2)
## purrr * 0.3.4 2020-04-17 [1] RSPM (R 4.0.0)
## R6 2.4.1 2019-11-12 [1] RSPM (R 4.0.0)
## Rcpp 1.0.5 2020-07-06 [1] RSPM (R 4.0.2)
## readr * 1.4.0 2020-10-05 [1] RSPM (R 4.0.2)
## readxl 1.3.1 2019-03-13 [1] RSPM (R 4.0.2)
## reprex 0.3.0 2019-05-16 [1] RSPM (R 4.0.0)
## rlang * 0.4.8 2020-10-08 [1] RSPM (R 4.0.2)
## rmarkdown 2.4 2020-09-30 [1] RSPM (R 4.0.2)
## rmdformats 0.3.7 2020-03-11 [1] RSPM (R 4.0.0)
## rstudioapi 0.11 2020-02-07 [1] RSPM (R 4.0.0)
## rvest 0.3.6 2020-07-25 [1] RSPM (R 4.0.2)
## scales * 1.1.1 2020-05-11 [1] RSPM (R 4.0.0)
## sessioninfo 1.1.1 2018-11-05 [1] RSPM (R 4.0.0)
## snakecase * 0.11.0 2019-05-25 [1] RSPM (R 4.0.0)
## stringi 1.5.3 2020-09-09 [1] RSPM (R 4.0.2)
## stringr * 1.4.0 2019-02-10 [1] RSPM (R 4.0.0)
## tibble * 3.0.4 2020-10-12 [1] RSPM (R 4.0.2)
## tidyr * 1.1.2 2020-08-27 [1] RSPM (R 4.0.2)
## tidyselect 1.1.0 2020-05-11 [1] RSPM (R 4.0.0)
## tidyverse * 1.3.0 2019-11-21 [1] RSPM (R 4.0.0)
## utf8 1.1.4 2018-05-24 [1] RSPM (R 4.0.0)
## vctrs * 0.3.4 2020-08-29 [1] RSPM (R 4.0.2)
## withr 2.3.0 2020-09-22 [1] RSPM (R 4.0.2)
## xfun 0.18 2020-09-29 [1] RSPM (R 4.0.2)
## xml2 1.3.2 2020-04-23 [1] RSPM (R 4.0.0)
## yaml 2.2.1 2020-02-01 [1] RSPM (R 4.0.0)
##
## [1] /usr/local/lib/R/site-library
## [2] /usr/local/lib/R/library